Overview

Dataset statistics

Number of variables97
Number of observations129
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.9 KiB
Average record size in memory777.0 B

Variable types

Categorical61
Numeric36

Alerts

HE has constant value ""Constant
VARSTA_ELIFT has constant value ""Constant
CHILD_clasif is highly imbalanced (60.5%)Imbalance
DAA_after_dg is highly imbalanced (72.9%)Imbalance
gastricV is highly imbalanced (72.9%)Imbalance
secondary_prophylaxy is highly imbalanced (80.1%)Imbalance
PVT_Thrombosis is highly imbalanced (81.0%)Imbalance
Ascites is highly imbalanced (50.5%)Imbalance
Previous_decomp is highly imbalanced (63.5%)Imbalance
Major_surgery is highly imbalanced (55.4%)Imbalance
Transfusion_IOP is highly imbalanced (50.5%)Imbalance
Resection_Margin is highly imbalanced (61.8%)Imbalance
Use_of_vasoactives is highly imbalanced (76.3%)Imbalance
Ventil_Mecan is highly imbalanced (69.6%)Imbalance
Number_of_Organ_Failures is highly imbalanced (76.0%)Imbalance
ACLF_Grade is highly imbalanced (78.4%)Imbalance
HRS_post_op is highly imbalanced (80.1%)Imbalance
disf_renala_nonHRS_post_op is highly imbalanced (50.5%)Imbalance
HE_post_op is highly imbalanced (84.1%)Imbalance
JaundiceBTgt3 is highly imbalanced (76.3%)Imbalance
HDS_postop is highly imbalanced (88.5%)Imbalance
FIB4_3.74 is highly imbalanced (63.5%)Imbalance
FIB_3.74 is highly imbalanced (63.5%)Imbalance
CRP_Albumine_ratio has 2 (1.6%) infinite valuesInfinite
df_index is uniformly distributedUniform
FIB4 has unique valuesUnique
df_index has unique valuesUnique
Spleen has 2 (1.6%) zerosZeros
LiverStiffnes has 9 (7.0%) zerosZeros
Creatinine has 2 (1.6%) zerosZeros
Albumine has 2 (1.6%) zerosZeros
Blood_Loss has 23 (17.8%) zerosZeros

Reproduction

Analysis started2023-12-14 09:16:58.235004
Analysis finished2023-12-14 09:17:56.592967
Duration58.36 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Survival
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
72 
0.0
56 
11.0
 
1

Length

Max length4
Median length3
Mean length3.0077519
Min length3

Characters and Unicode

Total characters388
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 72
55.8%
0.0 56
43.4%
11.0 1
 
0.8%

Length

2023-12-14T11:17:56.628527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:56.678361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 72
55.8%
0.0 56
43.4%
11.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 185
47.7%
. 129
33.2%
1 74
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 259
66.8%
Other Punctuation 129
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 185
71.4%
1 74
 
28.6%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 388
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 185
47.7%
. 129
33.2%
1 74
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 185
47.7%
. 129
33.2%
1 74
 
19.1%

CHILD_score
Real number (ℝ)

Distinct6
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4883721
Minimum5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:56.724919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median5
Q35
95-th percentile8
Maximum10
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0240566
Coefficient of variation (CV)0.18658658
Kurtosis4.7100387
Mean5.4883721
Median Absolute Deviation (MAD)0
Skewness2.2707936
Sum708
Variance1.0486919
MonotonicityNot monotonic
2023-12-14T11:17:56.777912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 98
76.0%
6 13
 
10.1%
7 8
 
6.2%
8 7
 
5.4%
9 2
 
1.6%
10 1
 
0.8%
ValueCountFrequency (%)
5 98
76.0%
6 13
 
10.1%
7 8
 
6.2%
8 7
 
5.4%
9 2
 
1.6%
10 1
 
0.8%
ValueCountFrequency (%)
10 1
 
0.8%
9 2
 
1.6%
8 7
 
5.4%
7 8
 
6.2%
6 13
 
10.1%
5 98
76.0%

CHILD_clasif
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
111 
1.0
17 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 111
86.0%
1.0 17
 
13.2%
3.0 1
 
0.8%

Length

2023-12-14T11:17:56.835116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:56.883463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 111
86.0%
1.0 17
 
13.2%
3.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 240
62.0%
. 129
33.3%
1 17
 
4.4%
3 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 240
93.0%
1 17
 
6.6%
3 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 240
62.0%
. 129
33.3%
1 17
 
4.4%
3 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 240
62.0%
. 129
33.3%
1 17
 
4.4%
3 1
 
0.3%

Nr_nodules
Real number (ℝ)

Distinct6
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3875969
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:56.929778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88661996
Coefficient of variation (CV)0.63896076
Kurtosis9.3601702
Mean1.3875969
Median Absolute Deviation (MAD)0
Skewness2.9089378
Sum179
Variance0.78609496
MonotonicityNot monotonic
2023-12-14T11:17:56.980571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 100
77.5%
2 17
 
13.2%
3 7
 
5.4%
4 2
 
1.6%
5 2
 
1.6%
6 1
 
0.8%
ValueCountFrequency (%)
1 100
77.5%
2 17
 
13.2%
3 7
 
5.4%
4 2
 
1.6%
5 2
 
1.6%
6 1
 
0.8%
ValueCountFrequency (%)
6 1
 
0.8%
5 2
 
1.6%
4 2
 
1.6%
3 7
 
5.4%
2 17
 
13.2%
1 100
77.5%

BCLC_class
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
61 
2.0
41 
3.0
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 61
47.3%
2.0 41
31.8%
3.0 27
20.9%

Length

2023-12-14T11:17:57.038173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.087035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 61
47.3%
2.0 41
31.8%
3.0 27
20.9%

Most occurring characters

ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 61
15.8%
2 41
 
10.6%
3 27
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
50.0%
1 61
23.6%
2 41
 
15.9%
3 27
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 61
15.8%
2 41
 
10.6%
3 27
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 61
15.8%
2 41
 
10.6%
3 27
 
7.0%

Age
Real number (ℝ)

Distinct29
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.449612
Minimum48
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:57.139284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile54
Q161
median65
Q370
95-th percentile76.6
Maximum83
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8121688
Coefficient of variation (CV)0.10408265
Kurtosis-0.11789065
Mean65.449612
Median Absolute Deviation (MAD)4
Skewness-0.051754494
Sum8443
Variance46.405644
MonotonicityNot monotonic
2023-12-14T11:17:57.203105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
65 12
 
9.3%
69 8
 
6.2%
62 8
 
6.2%
63 8
 
6.2%
59 8
 
6.2%
64 8
 
6.2%
66 7
 
5.4%
57 6
 
4.7%
73 6
 
4.7%
67 5
 
3.9%
Other values (19) 53
41.1%
ValueCountFrequency (%)
48 1
 
0.8%
50 2
 
1.6%
52 3
 
2.3%
54 2
 
1.6%
56 2
 
1.6%
57 6
4.7%
58 3
 
2.3%
59 8
6.2%
60 1
 
0.8%
61 5
3.9%
ValueCountFrequency (%)
83 1
 
0.8%
81 1
 
0.8%
78 2
 
1.6%
77 3
2.3%
76 3
2.3%
75 1
 
0.8%
74 5
3.9%
73 6
4.7%
72 5
3.9%
71 3
2.3%

Sex
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
101 
2.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 101
78.3%
2.0 28
 
21.7%

Length

2023-12-14T11:17:57.266984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.314410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 101
78.3%
2.0 28
 
21.7%

Most occurring characters

ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 101
26.1%
2 28
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
50.0%
1 101
39.1%
2 28
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 101
26.1%
2 28
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 101
26.1%
2 28
 
7.2%

Etiology
Categorical

Distinct5
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2.0
56 
1.0
41 
3.0
26 
5.0
 
4
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 56
43.4%
1.0 41
31.8%
3.0 26
20.2%
5.0 4
 
3.1%
4.0 2
 
1.6%

Length

2023-12-14T11:17:57.366507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.417733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 56
43.4%
1.0 41
31.8%
3.0 26
20.2%
5.0 4
 
3.1%
4.0 2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
2 56
14.5%
1 41
 
10.6%
3 26
 
6.7%
5 4
 
1.0%
4 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
50.0%
2 56
21.7%
1 41
 
15.9%
3 26
 
10.1%
5 4
 
1.6%
4 2
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
2 56
14.5%
1 41
 
10.6%
3 26
 
6.7%
5 4
 
1.0%
4 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
2 56
14.5%
1 41
 
10.6%
3 26
 
6.7%
5 4
 
1.0%
4 2
 
0.5%

DAA_before_dg
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
113 
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 113
87.6%
1.0 16
 
12.4%

Length

2023-12-14T11:17:57.475437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.522930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 113
87.6%
1.0 16
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 242
62.5%
. 129
33.3%
1 16
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 242
93.8%
1 16
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 242
62.5%
. 129
33.3%
1 16
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 242
62.5%
. 129
33.3%
1 16
 
4.1%

DAA_after_dg
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
123 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 123
95.3%
1.0 6
 
4.7%

Length

2023-12-14T11:17:57.574447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.620734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 123
95.3%
1.0 6
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 6
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 252
97.7%
1 6
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 6
 
1.6%

EV
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
92 
1.0
37 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 92
71.3%
1.0 37
28.7%

Length

2023-12-14T11:17:57.671439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.718272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 92
71.3%
1.0 37
28.7%

Most occurring characters

ValueCountFrequency (%)
0 221
57.1%
. 129
33.3%
1 37
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 221
85.7%
1 37
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 221
57.1%
. 129
33.3%
1 37
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 221
57.1%
. 129
33.3%
1 37
 
9.6%

Grade
Categorical

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
92 
1.0
18 
2.0
16 
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 92
71.3%
1.0 18
 
14.0%
2.0 16
 
12.4%
3.0 3
 
2.3%

Length

2023-12-14T11:17:57.769856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.820922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 92
71.3%
1.0 18
 
14.0%
2.0 16
 
12.4%
3.0 3
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 221
57.1%
. 129
33.3%
1 18
 
4.7%
2 16
 
4.1%
3 3
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 221
85.7%
1 18
 
7.0%
2 16
 
6.2%
3 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 221
57.1%
. 129
33.3%
1 18
 
4.7%
2 16
 
4.1%
3 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 221
57.1%
. 129
33.3%
1 18
 
4.7%
2 16
 
4.1%
3 3
 
0.8%

gastricV
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
123 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 123
95.3%
1.0 6
 
4.7%

Length

2023-12-14T11:17:57.876049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:57.923147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 123
95.3%
1.0 6
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 6
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 252
97.7%
1 6
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 6
 
1.6%

HTP_gastropathy
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
107 
1.0
22 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 107
82.9%
1.0 22
 
17.1%

Length

2023-12-14T11:17:57.973777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.022148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 107
82.9%
1.0 22
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 236
61.0%
. 129
33.3%
1 22
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 236
91.5%
1 22
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 236
61.0%
. 129
33.3%
1 22
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 236
61.0%
. 129
33.3%
1 22
 
5.7%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
110 
1.0
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 110
85.3%
1.0 19
 
14.7%

Length

2023-12-14T11:17:58.073339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.121342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110
85.3%
1.0 19
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 239
61.8%
. 129
33.3%
1 19
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 239
92.6%
1 19
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 239
61.8%
. 129
33.3%
1 19
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 239
61.8%
. 129
33.3%
1 19
 
4.9%

secondary_prophylaxy
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
125 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 125
96.9%
1.0 4
 
3.1%

Length

2023-12-14T11:17:58.172180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.220148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 125
96.9%
1.0 4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 254
65.6%
. 129
33.3%
1 4
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 254
98.4%
1 4
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 254
65.6%
. 129
33.3%
1 4
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254
65.6%
. 129
33.3%
1 4
 
1.0%

PVT_Thrombosis
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
123 
1.0
 
5
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 123
95.3%
1.0 5
 
3.9%
2.0 1
 
0.8%

Length

2023-12-14T11:17:58.270864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.320479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 123
95.3%
1.0 5
 
3.9%
2.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 5
 
1.3%
2 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 252
97.7%
1 5
 
1.9%
2 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 5
 
1.3%
2 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 252
65.1%
. 129
33.3%
1 5
 
1.3%
2 1
 
0.3%

Spleen
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.2796
Minimum0
Maximum200
Zeros2
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:58.375826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1119
median122
Q3140
95-th percentile178.4
Maximum200
Range200
Interquartile range (IQR)21

Descriptive statistics

Standard deviation29.333018
Coefficient of variation (CV)0.23046126
Kurtosis4.6748369
Mean127.2796
Median Absolute Deviation (MAD)12
Skewness-0.79421868
Sum16419.069
Variance860.42595
MonotonicityNot monotonic
2023-12-14T11:17:58.448924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
119 27
20.9%
129.4712644 15
 
11.6%
140 8
 
6.2%
150 7
 
5.4%
90 6
 
4.7%
110 6
 
4.7%
160 6
 
4.7%
120 5
 
3.9%
115 4
 
3.1%
125 3
 
2.3%
Other values (33) 42
32.6%
ValueCountFrequency (%)
0 2
 
1.6%
72 1
 
0.8%
77 1
 
0.8%
85 1
 
0.8%
87 1
 
0.8%
90 6
4.7%
100 3
2.3%
103 1
 
0.8%
105 2
 
1.6%
109 1
 
0.8%
ValueCountFrequency (%)
200 1
 
0.8%
195 1
 
0.8%
190 3
2.3%
180 2
 
1.6%
176 1
 
0.8%
170 2
 
1.6%
167 1
 
0.8%
165 1
 
0.8%
160 6
4.7%
158 1
 
0.8%

Splenomegaly_0no
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
65 
0
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 65
50.4%
0 64
49.6%

Length

2023-12-14T11:17:58.515051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.631673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 65
50.4%
0 64
49.6%

Most occurring characters

ValueCountFrequency (%)
1 65
50.4%
0 64
49.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 65
50.4%
0 64
49.6%

Most occurring scripts

ValueCountFrequency (%)
Common 129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 65
50.4%
0 64
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 65
50.4%
0 64
49.6%

Platelets
Real number (ℝ)

Distinct100
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.87597
Minimum24
Maximum385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:58.686964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile56.4
Q190
median141
Q3182
95-th percentile279.6
Maximum385
Range361
Interquartile range (IQR)92

Descriptive statistics

Standard deviation72.150776
Coefficient of variation (CV)0.49123609
Kurtosis0.76043307
Mean146.87597
Median Absolute Deviation (MAD)46
Skewness0.93075605
Sum18947
Variance5205.7345
MonotonicityNot monotonic
2023-12-14T11:17:58.766957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 4
 
3.1%
117 4
 
3.1%
67 3
 
2.3%
169 3
 
2.3%
145 3
 
2.3%
139 2
 
1.6%
89 2
 
1.6%
137 2
 
1.6%
95 2
 
1.6%
52 2
 
1.6%
Other values (90) 102
79.1%
ValueCountFrequency (%)
24 1
0.8%
42 1
0.8%
43 1
0.8%
49 1
0.8%
52 2
1.6%
56 1
0.8%
57 1
0.8%
58 1
0.8%
64 1
0.8%
66 2
1.6%
ValueCountFrequency (%)
385 1
0.8%
370 1
0.8%
330 1
0.8%
322 1
0.8%
307 1
0.8%
300 1
0.8%
280 1
0.8%
279 1
0.8%
274 1
0.8%
271 1
0.8%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
91 
1.0
38 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 91
70.5%
1.0 38
29.5%

Length

2023-12-14T11:17:58.831972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.879629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 91
70.5%
1.0 38
29.5%

Most occurring characters

ValueCountFrequency (%)
0 220
56.8%
. 129
33.3%
1 38
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 220
85.3%
1 38
 
14.7%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 220
56.8%
. 129
33.3%
1 38
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 220
56.8%
. 129
33.3%
1 38
 
9.8%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
101 
1.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 101
78.3%
1.0 28
 
21.7%

Length

2023-12-14T11:17:58.930875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:58.978677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 101
78.3%
1.0 28
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 230
59.4%
. 129
33.3%
1 28
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 230
89.1%
1 28
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 230
59.4%
. 129
33.3%
1 28
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 230
59.4%
. 129
33.3%
1 28
 
7.2%

HE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 129
100.0%

Length

2023-12-14T11:17:59.028637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.074647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 129
100.0%

Most occurring characters

ValueCountFrequency (%)
0 258
66.7%
. 129
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258
100.0%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 258
66.7%
. 129
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 258
66.7%
. 129
33.3%

Ascites
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
115 
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 115
89.1%
1.0 14
 
10.9%

Length

2023-12-14T11:17:59.122192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.169941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 115
89.1%
1.0 14
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244
94.6%
1 14
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

LiverStiffnes
Real number (ℝ)

ZEROS 

Distinct78
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.25969
Minimum0
Maximum75
Zeros9
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:59.225719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.5
median13.9
Q321.8
95-th percentile43.3
Maximum75
Range75
Interquartile range (IQR)11.3

Descriptive statistics

Standard deviation13.03972
Coefficient of variation (CV)0.75550141
Kurtosis4.040954
Mean17.25969
Median Absolute Deviation (MAD)5
Skewness1.7157921
Sum2226.5
Variance170.0343
MonotonicityNot monotonic
2023-12-14T11:17:59.295623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.9 35
27.1%
0 9
 
7.0%
24.1 2
 
1.6%
11.1 2
 
1.6%
11.8 2
 
1.6%
38 2
 
1.6%
14.3 2
 
1.6%
6.8 2
 
1.6%
9 2
 
1.6%
6.6 2
 
1.6%
Other values (68) 69
53.5%
ValueCountFrequency (%)
0 9
7.0%
3.3 1
 
0.8%
4 1
 
0.8%
4.3 1
 
0.8%
4.6 1
 
0.8%
4.7 1
 
0.8%
4.9 1
 
0.8%
5.8 1
 
0.8%
6 1
 
0.8%
6.1 1
 
0.8%
ValueCountFrequency (%)
75 1
0.8%
66.4 1
0.8%
55.1 1
0.8%
50 1
0.8%
48.9 1
0.8%
47 1
0.8%
46.5 1
0.8%
38.5 1
0.8%
38 2
1.6%
37.2 1
0.8%

FS_19kPa
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
91 
1.0
38 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 91
70.5%
1.0 38
29.5%

Length

2023-12-14T11:17:59.359576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.406912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 91
70.5%
1.0 38
29.5%

Most occurring characters

ValueCountFrequency (%)
0 220
56.8%
. 129
33.3%
1 38
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 220
85.3%
1 38
 
14.7%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 220
56.8%
. 129
33.3%
1 38
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 220
56.8%
. 129
33.3%
1 38
 
9.8%

FS_13.6
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
84 
1.0
45 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 84
65.1%
1.0 45
34.9%

Length

2023-12-14T11:17:59.458701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.506693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 84
65.1%
1.0 45
34.9%

Most occurring characters

ValueCountFrequency (%)
0 213
55.0%
. 129
33.3%
1 45
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 213
82.6%
1 45
 
17.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 213
55.0%
. 129
33.3%
1 45
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 213
55.0%
. 129
33.3%
1 45
 
11.6%

CSPH
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
71 
1.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 71
55.0%
1.0 58
45.0%

Length

2023-12-14T11:17:59.557227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.605871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 71
55.0%
1.0 58
45.0%

Most occurring characters

ValueCountFrequency (%)
0 200
51.7%
. 129
33.3%
1 58
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 200
77.5%
1 58
 
22.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 200
51.7%
. 129
33.3%
1 58
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 200
51.7%
. 129
33.3%
1 58
 
15.0%

HVPG
Real number (ℝ)

Distinct21
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.023256
Minimum2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:17:59.652508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q314
95-th percentile18
Maximum27
Range25
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.317226
Coefficient of variation (CV)0.53048891
Kurtosis0.15556989
Mean10.023256
Median Absolute Deviation (MAD)4
Skewness0.75075296
Sum1293
Variance28.272892
MonotonicityNot monotonic
2023-12-14T11:17:59.709258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
8 13
 
10.1%
4 13
 
10.1%
9 11
 
8.5%
7 11
 
8.5%
6 9
 
7.0%
13 9
 
7.0%
18 7
 
5.4%
3 7
 
5.4%
14 7
 
5.4%
10 7
 
5.4%
Other values (11) 35
27.1%
ValueCountFrequency (%)
2 2
 
1.6%
3 7
5.4%
4 13
10.1%
5 6
4.7%
6 9
7.0%
7 11
8.5%
8 13
10.1%
9 11
8.5%
10 7
5.4%
11 2
 
1.6%
ValueCountFrequency (%)
27 1
 
0.8%
24 2
 
1.6%
23 1
 
0.8%
21 2
 
1.6%
18 7
5.4%
17 4
3.1%
16 4
3.1%
15 5
3.9%
14 7
5.4%
13 9
7.0%

MELD_8
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
101 
0.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 101
78.3%
0.0 28
 
21.7%

Length

2023-12-14T11:17:59.765045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.812822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 101
78.3%
0.0 28
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 157
40.6%
. 129
33.3%
1 101
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
60.9%
1 101
39.1%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
40.6%
. 129
33.3%
1 101
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
40.6%
. 129
33.3%
1 101
26.1%

CSPH_MELD_10
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
96 
1.0
33 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 96
74.4%
1.0 33
 
25.6%

Length

2023-12-14T11:17:59.864212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:17:59.911429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 96
74.4%
1.0 33
 
25.6%

Most occurring characters

ValueCountFrequency (%)
0 225
58.1%
. 129
33.3%
1 33
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 225
87.2%
1 33
 
12.8%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 225
58.1%
. 129
33.3%
1 33
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 225
58.1%
. 129
33.3%
1 33
 
8.5%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
85 
1.0
44 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 85
65.9%
1.0 44
34.1%

Length

2023-12-14T11:17:59.961841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:00.009320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85
65.9%
1.0 44
34.1%

Most occurring characters

ValueCountFrequency (%)
0 214
55.3%
. 129
33.3%
1 44
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 214
82.9%
1 44
 
17.1%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 214
55.3%
. 129
33.3%
1 44
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 214
55.3%
. 129
33.3%
1 44
 
11.4%

MELD
Real number (ℝ)

Distinct13
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4806202
Minimum6
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.054527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.4
Q18
median9
Q311
95-th percentile16
Maximum19
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.79816
Coefficient of variation (CV)0.29514525
Kurtosis1.8705312
Mean9.4806202
Median Absolute Deviation (MAD)2
Skewness1.4106721
Sum1223
Variance7.8296996
MonotonicityNot monotonic
2023-12-14T11:18:00.113271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8 34
26.4%
7 22
17.1%
9 20
15.5%
11 14
10.9%
10 9
 
7.0%
6 7
 
5.4%
13 6
 
4.7%
12 5
 
3.9%
16 4
 
3.1%
14 4
 
3.1%
Other values (3) 4
 
3.1%
ValueCountFrequency (%)
6 7
 
5.4%
7 22
17.1%
8 34
26.4%
9 20
15.5%
10 9
 
7.0%
11 14
10.9%
12 5
 
3.9%
13 6
 
4.7%
14 4
 
3.1%
16 4
 
3.1%
ValueCountFrequency (%)
19 2
 
1.6%
18 1
 
0.8%
17 1
 
0.8%
16 4
 
3.1%
14 4
 
3.1%
13 6
 
4.7%
12 5
 
3.9%
11 14
10.9%
10 9
7.0%
9 20
15.5%

MELDNa
Real number (ℝ)

Distinct15
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.062016
Minimum6
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.167729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q18
median9
Q311
95-th percentile16.6
Maximum26
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4770496
Coefficient of variation (CV)0.34556194
Kurtosis4.2182314
Mean10.062016
Median Absolute Deviation (MAD)1
Skewness1.8443386
Sum1298
Variance12.089874
MonotonicityNot monotonic
2023-12-14T11:18:00.228984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8 35
27.1%
9 20
15.5%
7 15
11.6%
10 12
 
9.3%
11 10
 
7.8%
13 8
 
6.2%
12 6
 
4.7%
6 6
 
4.7%
14 6
 
4.7%
16 4
 
3.1%
Other values (5) 7
 
5.4%
ValueCountFrequency (%)
6 6
 
4.7%
7 15
11.6%
8 35
27.1%
9 20
15.5%
10 12
 
9.3%
11 10
 
7.8%
12 6
 
4.7%
13 8
 
6.2%
14 6
 
4.7%
16 4
 
3.1%
ValueCountFrequency (%)
26 1
 
0.8%
22 1
 
0.8%
20 2
 
1.6%
19 2
 
1.6%
17 1
 
0.8%
16 4
 
3.1%
14 6
4.7%
13 8
6.2%
12 6
4.7%
11 10
7.8%

AST
Real number (ℝ)

Distinct79
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.457364
Minimum13
Maximum331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.297581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18
Q126.2
median36
Q354
95-th percentile106.8
Maximum331
Range318
Interquartile range (IQR)27.8

Descriptive statistics

Standard deviation42.517342
Coefficient of variation (CV)0.87741754
Kurtosis20.902677
Mean48.457364
Median Absolute Deviation (MAD)13
Skewness4.0352866
Sum6251
Variance1807.7243
MonotonicityNot monotonic
2023-12-14T11:18:00.365757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 6
 
4.7%
29 5
 
3.9%
34 4
 
3.1%
36 4
 
3.1%
31 3
 
2.3%
51 3
 
2.3%
33 3
 
2.3%
26 3
 
2.3%
52 3
 
2.3%
55 3
 
2.3%
Other values (69) 92
71.3%
ValueCountFrequency (%)
13 1
 
0.8%
14 1
 
0.8%
15 1
 
0.8%
17 2
 
1.6%
18 3
2.3%
19 3
2.3%
20 2
 
1.6%
21 3
2.3%
22 2
 
1.6%
23 6
4.7%
ValueCountFrequency (%)
331 1
 
0.8%
261 1
 
0.8%
226 1
 
0.8%
132 1
 
0.8%
117 1
 
0.8%
116 1
 
0.8%
108 1
 
0.8%
105 1
 
0.8%
103 1
 
0.8%
90 3
2.3%

ALT
Real number (ℝ)

Distinct72
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.226357
Minimum7
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.435935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11.4
Q121
median29
Q352
95-th percentile104.2
Maximum265
Range258
Interquartile range (IQR)31

Descriptive statistics

Standard deviation37.192003
Coefficient of variation (CV)0.88077698
Kurtosis12.963324
Mean42.226357
Median Absolute Deviation (MAD)11
Skewness3.0469509
Sum5447.2
Variance1383.2451
MonotonicityNot monotonic
2023-12-14T11:18:00.505482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 6
 
4.7%
40 5
 
3.9%
17 5
 
3.9%
29 4
 
3.1%
18 4
 
3.1%
31 4
 
3.1%
19 4
 
3.1%
26 4
 
3.1%
21 3
 
2.3%
36 3
 
2.3%
Other values (62) 87
67.4%
ValueCountFrequency (%)
7 1
 
0.8%
9 2
 
1.6%
10 2
 
1.6%
11 2
 
1.6%
12 3
2.3%
13 2
 
1.6%
14 2
 
1.6%
15 2
 
1.6%
17 5
3.9%
18 4
3.1%
ValueCountFrequency (%)
265 1
0.8%
217 1
0.8%
139 2
1.6%
130 1
0.8%
110 1
0.8%
105 1
0.8%
103 1
0.8%
99 1
0.8%
98 1
0.8%
94 1
0.8%

FA
Real number (ℝ)

Distinct109
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.52713
Minimum43.4
Maximum595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.641910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum43.4
5-th percentile59.44
Q1111
median203
Q3277
95-th percentile467.6
Maximum595
Range551.6
Interquartile range (IQR)166

Descriptive statistics

Standard deviation121.4634
Coefficient of variation (CV)0.55582756
Kurtosis1.0730533
Mean218.52713
Median Absolute Deviation (MAD)76
Skewness0.96002144
Sum28190
Variance14753.358
MonotonicityNot monotonic
2023-12-14T11:18:00.709427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203 4
 
3.1%
232 3
 
2.3%
103 3
 
2.3%
329 2
 
1.6%
185 2
 
1.6%
134 2
 
1.6%
188 2
 
1.6%
199 2
 
1.6%
371 2
 
1.6%
236 2
 
1.6%
Other values (99) 105
81.4%
ValueCountFrequency (%)
43.4 1
0.8%
45.4 1
0.8%
48 1
0.8%
51.6 1
0.8%
57 1
0.8%
57.3 1
0.8%
58.2 1
0.8%
61.3 1
0.8%
69.6 1
0.8%
69.7 1
0.8%
ValueCountFrequency (%)
595 1
0.8%
594 1
0.8%
563 1
0.8%
544 1
0.8%
527 1
0.8%
512 1
0.8%
490 1
0.8%
434 1
0.8%
427 1
0.8%
390 1
0.8%

GGT
Real number (ℝ)

Distinct93
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.268217
Minimum16
Maximum367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.776044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile21.4
Q134
median58
Q383
95-th percentile230.6
Maximum367
Range351
Interquartile range (IQR)49

Descriptive statistics

Standard deviation64.570554
Coefficient of variation (CV)0.84662466
Kurtosis4.6479922
Mean76.268217
Median Absolute Deviation (MAD)25
Skewness2.0845821
Sum9838.6
Variance4169.3564
MonotonicityNot monotonic
2023-12-14T11:18:00.849317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 5
 
3.9%
40 4
 
3.1%
29 3
 
2.3%
24 3
 
2.3%
36 3
 
2.3%
26 3
 
2.3%
114 3
 
2.3%
42 3
 
2.3%
23 2
 
1.6%
55 2
 
1.6%
Other values (83) 98
76.0%
ValueCountFrequency (%)
16 1
 
0.8%
17 2
1.6%
18 1
 
0.8%
19 1
 
0.8%
20.7 1
 
0.8%
21 1
 
0.8%
22 1
 
0.8%
22.5 1
 
0.8%
23 2
1.6%
24 3
2.3%
ValueCountFrequency (%)
367 1
0.8%
291 1
0.8%
278 1
0.8%
267 1
0.8%
252 1
0.8%
247 1
0.8%
235 1
0.8%
224 1
0.8%
202 1
0.8%
198 1
0.8%

Bilirubine
Real number (ℝ)

Distinct50
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0736163
Minimum0.3
Maximum3.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:00.920020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.6
median0.8905
Q31.3
95-th percentile2.5
Maximum3.4
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.64166933
Coefficient of variation (CV)0.59767101
Kurtosis2.7313852
Mean1.0736163
Median Absolute Deviation (MAD)0.3205
Skewness1.6513466
Sum138.4965
Variance0.41173953
MonotonicityNot monotonic
2023-12-14T11:18:00.992554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 16
 
12.4%
0.7 10
 
7.8%
0.8 9
 
7.0%
0.9 9
 
7.0%
0.6 8
 
6.2%
1.3 7
 
5.4%
1.4 5
 
3.9%
1.7 5
 
3.9%
1.1 5
 
3.9%
1 4
 
3.1%
Other values (40) 51
39.5%
ValueCountFrequency (%)
0.3 1
 
0.8%
0.4 4
 
3.1%
0.5 16
12.4%
0.516 1
 
0.8%
0.518 1
 
0.8%
0.552 1
 
0.8%
0.56 1
 
0.8%
0.57 1
 
0.8%
0.6 8
6.2%
0.63 1
 
0.8%
ValueCountFrequency (%)
3.4 1
0.8%
3.2 1
0.8%
3.1 2
1.6%
2.9 1
0.8%
2.7 1
0.8%
2.5 2
1.6%
2.2 1
0.8%
2.06 1
0.8%
1.99 1
0.8%
1.93 1
0.8%

Creatinine
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90147287
Minimum0
Maximum8.8
Zeros2
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.065909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q10.67
median0.81
Q30.98
95-th percentile1.276
Maximum8.8
Range8.8
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.77447371
Coefficient of variation (CV)0.85912038
Kurtosis86.155041
Mean0.90147287
Median Absolute Deviation (MAD)0.15
Skewness8.5963049
Sum116.29
Variance0.59980953
MonotonicityNot monotonic
2023-12-14T11:18:01.137460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.82 5
 
3.9%
0.57 5
 
3.9%
0.79 5
 
3.9%
0.66 5
 
3.9%
0.73 4
 
3.1%
1.03 4
 
3.1%
1 4
 
3.1%
0.9 4
 
3.1%
0.7 3
 
2.3%
0.93 3
 
2.3%
Other values (57) 87
67.4%
ValueCountFrequency (%)
0 2
 
1.6%
0.37 1
 
0.8%
0.38 1
 
0.8%
0.4 1
 
0.8%
0.44 1
 
0.8%
0.5 2
 
1.6%
0.52 1
 
0.8%
0.53 1
 
0.8%
0.56 1
 
0.8%
0.57 5
3.9%
ValueCountFrequency (%)
8.8 1
0.8%
3.13 1
0.8%
2.05 1
0.8%
1.56 1
0.8%
1.42 1
0.8%
1.4 1
0.8%
1.32 1
0.8%
1.21 1
0.8%
1.19 2
1.6%
1.16 1
0.8%

Neutrophils
Real number (ℝ)

Distinct115
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6537209
Minimum0.66
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.206363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile1.95
Q12.8
median3.83
Q35.13
95-th percentile8.846
Maximum46
Range45.34
Interquartile range (IQR)2.33

Descriptive statistics

Standard deviation4.3654712
Coefficient of variation (CV)0.93806038
Kurtosis63.424955
Mean4.6537209
Median Absolute Deviation (MAD)1.21
Skewness6.9799301
Sum600.33
Variance19.057339
MonotonicityNot monotonic
2023-12-14T11:18:01.274865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14 3
 
2.3%
2.2 2
 
1.6%
3.47 2
 
1.6%
5.42 2
 
1.6%
1.99 2
 
1.6%
4.95 2
 
1.6%
4.57 2
 
1.6%
3.6 2
 
1.6%
4.84 2
 
1.6%
3.99 2
 
1.6%
Other values (105) 108
83.7%
ValueCountFrequency (%)
0.66 1
0.8%
0.74 1
0.8%
0.97 1
0.8%
1.78 1
0.8%
1.79 1
0.8%
1.88 1
0.8%
1.95 2
1.6%
1.99 2
1.6%
2.03 1
0.8%
2.09 2
1.6%
ValueCountFrequency (%)
46 1
0.8%
14.53 1
0.8%
13.25 1
0.8%
12.58 1
0.8%
12.2 1
0.8%
11.46 1
0.8%
8.93 1
0.8%
8.72 1
0.8%
7.8 1
0.8%
7.61 1
0.8%

Lymphocytes
Real number (ℝ)

Distinct107
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5827907
Minimum0.23
Maximum10.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.344855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.532
Q10.94
median1.41
Q31.95
95-th percentile2.836
Maximum10.59
Range10.36
Interquartile range (IQR)1.01

Descriptive statistics

Standard deviation1.0921173
Coefficient of variation (CV)0.68999479
Kurtosis35.587749
Mean1.5827907
Median Absolute Deviation (MAD)0.51
Skewness4.6394039
Sum204.18
Variance1.1927203
MonotonicityNot monotonic
2023-12-14T11:18:01.417664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.27 4
 
3.1%
1.2 3
 
2.3%
0.65 2
 
1.6%
2.05 2
 
1.6%
1.56 2
 
1.6%
1.76 2
 
1.6%
1.35 2
 
1.6%
1.95 2
 
1.6%
1.05 2
 
1.6%
0.86 2
 
1.6%
Other values (97) 106
82.2%
ValueCountFrequency (%)
0.23 1
0.8%
0.38 1
0.8%
0.43 1
0.8%
0.5 1
0.8%
0.51 1
0.8%
0.52 2
1.6%
0.55 1
0.8%
0.59 1
0.8%
0.62 1
0.8%
0.63 1
0.8%
ValueCountFrequency (%)
10.59 1
0.8%
4.01 1
0.8%
3.94 1
0.8%
3.44 1
0.8%
3.18 1
0.8%
3.09 1
0.8%
2.84 1
0.8%
2.83 1
0.8%
2.8 1
0.8%
2.78 1
0.8%

Natrium
Real number (ℝ)

Distinct27
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.98062
Minimum104
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.480494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile134.4
Q1137.5
median139.7
Q3141
95-th percentile144
Maximum147
Range43
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation4.1359528
Coefficient of variation (CV)0.029759206
Kurtosis39.47434
Mean138.98062
Median Absolute Deviation (MAD)1.7
Skewness-4.7196006
Sum17928.5
Variance17.106106
MonotonicityNot monotonic
2023-12-14T11:18:01.538078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
141 19
14.7%
140 19
14.7%
138 18
14.0%
137 10
 
7.8%
139 9
 
7.0%
142 8
 
6.2%
136 7
 
5.4%
135 5
 
3.9%
134 4
 
3.1%
144 4
 
3.1%
Other values (17) 26
20.2%
ValueCountFrequency (%)
104 1
 
0.8%
132 2
 
1.6%
134 4
 
3.1%
135 5
3.9%
135.9 1
 
0.8%
136 7
5.4%
136.7 1
 
0.8%
136.8 1
 
0.8%
137 10
7.8%
137.5 1
 
0.8%
ValueCountFrequency (%)
147 1
 
0.8%
145 4
 
3.1%
144 4
 
3.1%
143 3
 
2.3%
142.9 1
 
0.8%
142 8
6.2%
141 19
14.7%
140.2 1
 
0.8%
140 19
14.7%
139.9 1
 
0.8%

IP
Real number (ℝ)

Distinct95
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.134109
Minimum40
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.601155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile50.22
Q165.2
median80
Q388.7
95-th percentile102.6
Maximum111
Range71
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation16.085735
Coefficient of variation (CV)0.20854243
Kurtosis-0.64412643
Mean77.134109
Median Absolute Deviation (MAD)10.1
Skewness-0.24800065
Sum9950.3
Variance258.75086
MonotonicityNot monotonic
2023-12-14T11:18:01.673717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 4
 
3.1%
86 4
 
3.1%
70 3
 
2.3%
59 3
 
2.3%
77 3
 
2.3%
72 3
 
2.3%
87 2
 
1.6%
87.7 2
 
1.6%
95 2
 
1.6%
90.1 2
 
1.6%
Other values (85) 101
78.3%
ValueCountFrequency (%)
40 1
0.8%
43 1
0.8%
44.1 1
0.8%
45.4 1
0.8%
49 2
1.6%
49.7 1
0.8%
51 1
0.8%
52 1
0.8%
52.4 1
0.8%
53 1
0.8%
ValueCountFrequency (%)
111 1
0.8%
107 1
0.8%
106.3 1
0.8%
106 1
0.8%
105.3 1
0.8%
103 2
1.6%
102 1
0.8%
99.7 1
0.8%
99 1
0.8%
98.9 1
0.8%

INR
Real number (ℝ)

Distinct60
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2284496
Minimum0.92
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.747855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.92
5-th percentile0.984
Q11.1
median1.18
Q31.32
95-th percentile1.61
Maximum2
Range1.08
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.19242064
Coefficient of variation (CV)0.15663698
Kurtosis1.3880531
Mean1.2284496
Median Absolute Deviation (MAD)0.1
Skewness1.1064638
Sum158.47
Variance0.037025703
MonotonicityNot monotonic
2023-12-14T11:18:01.822542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
6.2%
1.15 6
 
4.7%
1.19 5
 
3.9%
1.12 5
 
3.9%
1.26 4
 
3.1%
1.2 4
 
3.1%
1.16 4
 
3.1%
1.17 4
 
3.1%
1.11 4
 
3.1%
1.04 4
 
3.1%
Other values (50) 81
62.8%
ValueCountFrequency (%)
0.92 1
 
0.8%
0.95 1
 
0.8%
0.96 2
1.6%
0.97 1
 
0.8%
0.98 2
1.6%
0.99 1
 
0.8%
1 2
1.6%
1.01 1
 
0.8%
1.02 2
1.6%
1.03 3
2.3%
ValueCountFrequency (%)
2 1
0.8%
1.71 2
1.6%
1.65 2
1.6%
1.62 1
0.8%
1.61 2
1.6%
1.57 1
0.8%
1.55 1
0.8%
1.54 1
0.8%
1.53 1
0.8%
1.52 1
0.8%

Albumine
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9803876
Minimum0
Maximum5.6
Zeros2
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:01.890932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.904
Q13.7
median4.1
Q34.35
95-th percentile5.096
Maximum5.6
Range5.6
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.76952238
Coefficient of variation (CV)0.1933285
Kurtosis9.8257764
Mean3.9803876
Median Absolute Deviation (MAD)0.3
Skewness-2.0807651
Sum513.47
Variance0.59216469
MonotonicityNot monotonic
2023-12-14T11:18:01.959822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8 10
 
7.8%
4.2 10
 
7.8%
4.1 9
 
7.0%
3.9 8
 
6.2%
4.3 8
 
6.2%
4.4 7
 
5.4%
3.7 6
 
4.7%
3.6 6
 
4.7%
4 5
 
3.9%
4.5 4
 
3.1%
Other values (43) 56
43.4%
ValueCountFrequency (%)
0 2
1.6%
2.4 1
0.8%
2.8 2
1.6%
2.9 2
1.6%
2.91 1
0.8%
2.99 1
0.8%
3 2
1.6%
3.03 1
0.8%
3.1 1
0.8%
3.14 1
0.8%
ValueCountFrequency (%)
5.6 1
0.8%
5.5 1
0.8%
5.3 1
0.8%
5.2 2
1.6%
5.1 2
1.6%
5.09 1
0.8%
5.02 1
0.8%
4.94 1
0.8%
4.9 1
0.8%
4.84 1
0.8%

CRP
Real number (ℝ)

Distinct63
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7726136
Minimum0.01
Maximum18.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:02.100698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.17
Q10.43
median1.2
Q31.7726136
95-th percentile6.288
Maximum18.4
Range18.39
Interquartile range (IQR)1.3426136

Descriptive statistics

Standard deviation2.4585195
Coefficient of variation (CV)1.386946
Kurtosis20.125453
Mean1.7726136
Median Absolute Deviation (MAD)0.7
Skewness3.9410779
Sum228.66716
Variance6.044318
MonotonicityNot monotonic
2023-12-14T11:18:02.172856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.772613636 41
31.8%
0.8 5
 
3.9%
0.3 5
 
3.9%
0.45 4
 
3.1%
0.43 4
 
3.1%
0.2 3
 
2.3%
0.52 2
 
1.6%
0.38 2
 
1.6%
3.43 2
 
1.6%
0.36 2
 
1.6%
Other values (53) 59
45.7%
ValueCountFrequency (%)
0.01 1
 
0.8%
0.02 1
 
0.8%
0.06 1
 
0.8%
0.07 2
 
1.6%
0.14 1
 
0.8%
0.15 1
 
0.8%
0.2 3
2.3%
0.29 1
 
0.8%
0.3 5
3.9%
0.31 2
 
1.6%
ValueCountFrequency (%)
18.4 1
0.8%
13.1 1
0.8%
11 1
0.8%
7 1
0.8%
6.73 1
0.8%
6.65 1
0.8%
6.48 1
0.8%
6 1
0.8%
5.4 1
0.8%
5 1
0.8%

AFP
Real number (ℝ)

Distinct84
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.877054
Minimum0.91
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:02.246283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.91
5-th percentile2.58
Q14.9
median8.75
Q333.2
95-th percentile400
Maximum1000
Range999.09
Interquartile range (IQR)28.3

Descriptive statistics

Standard deviation164.03607
Coefficient of variation (CV)2.1618666
Kurtosis13.435313
Mean75.877054
Median Absolute Deviation (MAD)5.17
Skewness3.324912
Sum9788.14
Variance26907.833
MonotonicityNot monotonic
2023-12-14T11:18:02.317455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.75 19
 
14.7%
400 10
 
7.8%
6 3
 
2.3%
2 3
 
2.3%
2.8 3
 
2.3%
4.2 3
 
2.3%
6.2 3
 
2.3%
19.8 2
 
1.6%
5 2
 
1.6%
3.9 2
 
1.6%
Other values (74) 79
61.2%
ValueCountFrequency (%)
0.91 1
 
0.8%
2 3
2.3%
2.2 1
 
0.8%
2.3 1
 
0.8%
2.5 1
 
0.8%
2.7 1
 
0.8%
2.8 3
2.3%
2.83 1
 
0.8%
2.86 1
 
0.8%
2.9 1
 
0.8%
ValueCountFrequency (%)
1000 1
 
0.8%
957 1
 
0.8%
400 10
7.8%
351 1
 
0.8%
314 1
 
0.8%
310.5 1
 
0.8%
261 1
 
0.8%
259.9 1
 
0.8%
229.8 1
 
0.8%
196.1 1
 
0.8%

Previous_decomp
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
120 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 120
93.0%
1.0 9
 
7.0%

Length

2023-12-14T11:18:02.378340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:02.426036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 120
93.0%
1.0 9
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
96.5%
1 9
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Segment
Categorical

Distinct5
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
68 
2.0
47 
3.0
4.0
 
4
7.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 68
52.7%
2.0 47
36.4%
3.0 9
 
7.0%
4.0 4
 
3.1%
7.0 1
 
0.8%

Length

2023-12-14T11:18:02.475913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:02.528339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 68
52.7%
2.0 47
36.4%
3.0 9
 
7.0%
4.0 4
 
3.1%
7.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 68
17.6%
2 47
 
12.1%
3 9
 
2.3%
4 4
 
1.0%
7 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
50.0%
1 68
26.4%
2 47
 
18.2%
3 9
 
3.5%
4 4
 
1.6%
7 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 68
17.6%
2 47
 
12.1%
3 9
 
2.3%
4 4
 
1.0%
7 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 129
33.3%
0 129
33.3%
1 68
17.6%
2 47
 
12.1%
3 9
 
2.3%
4 4
 
1.0%
7 1
 
0.3%

Major_surgery
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
117 
1.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 117
90.7%
1.0 12
 
9.3%

Length

2023-12-14T11:18:02.584315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:02.633023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 117
90.7%
1.0 12
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 246
63.6%
. 129
33.3%
1 12
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 246
95.3%
1 12
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 246
63.6%
. 129
33.3%
1 12
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 246
63.6%
. 129
33.3%
1 12
 
3.1%

Size_each_nodule
Real number (ℝ)

Distinct31
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5697674
Minimum1.3
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:02.682625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2
Q13
median3.5
Q34.5
95-th percentile9
Maximum60
Range58.7
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation5.5442482
Coefficient of variation (CV)1.2132452
Kurtosis79.425588
Mean4.5697674
Median Absolute Deviation (MAD)0.5
Skewness8.2015231
Sum589.5
Variance30.738688
MonotonicityNot monotonic
2023-12-14T11:18:02.744180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.5 36
27.9%
3 15
11.6%
4 12
 
9.3%
2 8
 
6.2%
2.5 7
 
5.4%
5 5
 
3.9%
6 5
 
3.9%
4.5 4
 
3.1%
7 4
 
3.1%
2.3 3
 
2.3%
Other values (21) 30
23.3%
ValueCountFrequency (%)
1.3 1
 
0.8%
1.5 3
 
2.3%
2 8
6.2%
2.2 1
 
0.8%
2.3 3
 
2.3%
2.5 7
5.4%
2.6 2
 
1.6%
2.7 3
 
2.3%
2.8 1
 
0.8%
3 15
11.6%
ValueCountFrequency (%)
60 1
 
0.8%
20 1
 
0.8%
15.3 1
 
0.8%
12 2
 
1.6%
11 1
 
0.8%
9 2
 
1.6%
8 2
 
1.6%
7.3 1
 
0.8%
7 4
3.1%
6 5
3.9%

Size_total
Real number (ℝ)

Distinct44
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6127907
Minimum0.4
Maximum15.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:02.807928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum15.3
Range14.9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4011413
Coefficient of variation (CV)0.52053984
Kurtosis3.1664464
Mean4.6127907
Median Absolute Deviation (MAD)1.5
Skewness1.4200073
Sum595.05
Variance5.7654797
MonotonicityNot monotonic
2023-12-14T11:18:02.878463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 18
 
14.0%
4 12
 
9.3%
6 11
 
8.5%
2 8
 
6.2%
2.5 7
 
5.4%
4.5 6
 
4.7%
5 6
 
4.7%
7 5
 
3.9%
6.5 4
 
3.1%
2.3 3
 
2.3%
Other values (34) 49
38.0%
ValueCountFrequency (%)
0.4 1
 
0.8%
0.9 1
 
0.8%
1.5 2
 
1.6%
2 8
6.2%
2.1 1
 
0.8%
2.2 1
 
0.8%
2.3 3
 
2.3%
2.5 7
5.4%
2.6 1
 
0.8%
2.7 3
 
2.3%
ValueCountFrequency (%)
15.3 1
 
0.8%
12 2
 
1.6%
11 1
 
0.8%
10 1
 
0.8%
9.7 1
 
0.8%
9 2
 
1.6%
8 3
2.3%
7.3 2
 
1.6%
7.2 1
 
0.8%
7 5
3.9%

tu_more_than_3
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
70 
1.0
59 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 70
54.3%
1.0 59
45.7%

Length

2023-12-14T11:18:02.944711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:02.991918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 70
54.3%
1.0 59
45.7%

Most occurring characters

ValueCountFrequency (%)
0 199
51.4%
. 129
33.3%
1 59
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 199
77.1%
1 59
 
22.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 199
51.4%
. 129
33.3%
1 59
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 199
51.4%
. 129
33.3%
1 59
 
15.2%

Laparoscopy
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
99 
1.0
30 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 99
76.7%
1.0 30
 
23.3%

Length

2023-12-14T11:18:03.043323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:03.090680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 99
76.7%
1.0 30
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 228
58.9%
. 129
33.3%
1 30
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228
88.4%
1 30
 
11.6%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228
58.9%
. 129
33.3%
1 30
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228
58.9%
. 129
33.3%
1 30
 
7.8%

RFA_IOP
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
76 
1.0
53 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 76
58.9%
1.0 53
41.1%

Length

2023-12-14T11:18:03.142256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:03.189371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 76
58.9%
1.0 53
41.1%

Most occurring characters

ValueCountFrequency (%)
0 205
53.0%
. 129
33.3%
1 53
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 205
79.5%
1 53
 
20.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 205
53.0%
. 129
33.3%
1 53
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 205
53.0%
. 129
33.3%
1 53
 
13.7%

Anesthesia_Duration_min
Real number (ℝ)

Distinct28
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.9845
Minimum60
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:03.242069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile87
Q1110
median130
Q3150
95-th percentile200
Maximum270
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation36.796311
Coefficient of variation (CV)0.27669625
Kurtosis2.2397097
Mean132.9845
Median Absolute Deviation (MAD)20
Skewness0.99024096
Sum17155
Variance1353.9685
MonotonicityNot monotonic
2023-12-14T11:18:03.299744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
120 19
14.7%
150 18
14.0%
130 17
13.2%
100 12
9.3%
140 11
8.5%
90 9
 
7.0%
180 6
 
4.7%
110 5
 
3.9%
60 4
 
3.1%
160 2
 
1.6%
Other values (18) 26
20.2%
ValueCountFrequency (%)
60 4
 
3.1%
80 2
 
1.6%
85 1
 
0.8%
90 9
7.0%
100 12
9.3%
105 2
 
1.6%
110 5
 
3.9%
115 1
 
0.8%
120 19
14.7%
125 2
 
1.6%
ValueCountFrequency (%)
270 1
 
0.8%
255 1
 
0.8%
254 1
 
0.8%
220 1
 
0.8%
210 2
 
1.6%
200 2
 
1.6%
190 1
 
0.8%
180 6
4.7%
175 1
 
0.8%
170 2
 
1.6%

Blood_Loss
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.09302
Minimum0
Maximum2000
Zeros23
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:03.356188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180
median150
Q3300
95-th percentile1120
Maximum2000
Range2000
Interquartile range (IQR)220

Descriptive statistics

Standard deviation365.3384
Coefficient of variation (CV)1.3184684
Kurtosis6.4477553
Mean277.09302
Median Absolute Deviation (MAD)135
Skewness2.4569734
Sum35745
Variance133472.15
MonotonicityNot monotonic
2023-12-14T11:18:03.418871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 23
17.8%
100 20
15.5%
150 19
14.7%
200 12
9.3%
300 11
8.5%
50 8
 
6.2%
400 7
 
5.4%
500 5
 
3.9%
1500 4
 
3.1%
250 4
 
3.1%
Other values (12) 16
12.4%
ValueCountFrequency (%)
0 23
17.8%
15 1
 
0.8%
50 8
 
6.2%
80 1
 
0.8%
100 20
15.5%
150 19
14.7%
200 12
9.3%
250 4
 
3.1%
300 11
8.5%
350 1
 
0.8%
ValueCountFrequency (%)
2000 1
 
0.8%
1500 4
3.1%
1300 1
 
0.8%
1200 1
 
0.8%
1000 2
 
1.6%
900 1
 
0.8%
800 2
 
1.6%
700 2
 
1.6%
600 2
 
1.6%
500 5
3.9%

Surgery_Duration_min
Real number (ℝ)

Distinct29
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.83721
Minimum40
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:03.479806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile77
Q1105
median120
Q3150
95-th percentile196
Maximum270
Range230
Interquartile range (IQR)45

Descriptive statistics

Standard deviation38.507059
Coefficient of variation (CV)0.29657953
Kurtosis1.8765007
Mean129.83721
Median Absolute Deviation (MAD)25
Skewness0.86952554
Sum16749
Variance1482.7936
MonotonicityNot monotonic
2023-12-14T11:18:03.541074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
120 27
20.9%
150 20
15.5%
90 12
9.3%
140 8
 
6.2%
180 8
 
6.2%
100 8
 
6.2%
105 5
 
3.9%
130 5
 
3.9%
60 4
 
3.1%
210 3
 
2.3%
Other values (19) 29
22.5%
ValueCountFrequency (%)
40 1
 
0.8%
60 4
 
3.1%
75 2
 
1.6%
80 2
 
1.6%
85 1
 
0.8%
90 12
9.3%
95 1
 
0.8%
100 8
6.2%
105 5
3.9%
110 3
 
2.3%
ValueCountFrequency (%)
270 1
 
0.8%
255 1
 
0.8%
254 1
 
0.8%
210 3
 
2.3%
200 1
 
0.8%
190 1
 
0.8%
180 8
6.2%
175 1
 
0.8%
170 1
 
0.8%
160 2
 
1.6%

Transfusion_IOP
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
115 
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 115
89.1%
1.0 14
 
10.9%

Length

2023-12-14T11:18:03.600367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:03.648506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 115
89.1%
1.0 14
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244
94.6%
1 14
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
112 
1.0
17 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 112
86.8%
1.0 17
 
13.2%

Length

2023-12-14T11:18:03.699502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:03.747901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112
86.8%
1.0 17
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 241
62.3%
. 129
33.3%
1 17
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 241
93.4%
1 17
 
6.6%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 241
62.3%
. 129
33.3%
1 17
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 241
62.3%
. 129
33.3%
1 17
 
4.4%

Resection_Margin
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
112 
1.0
16 
0.4
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 112
86.8%
1.0 16
 
12.4%
0.4 1
 
0.8%

Length

2023-12-14T11:18:03.799123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:03.848927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112
86.8%
1.0 16
 
12.4%
0.4 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 241
62.3%
. 129
33.3%
1 16
 
4.1%
4 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 241
93.4%
1 16
 
6.2%
4 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 241
62.3%
. 129
33.3%
1 16
 
4.1%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 241
62.3%
. 129
33.3%
1 16
 
4.1%
4 1
 
0.3%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
102 
1.0
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 102
79.1%
1.0 27
 
20.9%

Length

2023-12-14T11:18:03.901518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:03.950059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 102
79.1%
1.0 27
 
20.9%

Most occurring characters

ValueCountFrequency (%)
0 231
59.7%
. 129
33.3%
1 27
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 231
89.5%
1 27
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 231
59.7%
. 129
33.3%
1 27
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 231
59.7%
. 129
33.3%
1 27
 
7.0%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
86 
0.0
43 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 86
66.7%
0.0 43
33.3%

Length

2023-12-14T11:18:04.069047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.116755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 86
66.7%
0.0 43
33.3%

Most occurring characters

ValueCountFrequency (%)
0 172
44.4%
. 129
33.3%
1 86
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
66.7%
1 86
33.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
44.4%
. 129
33.3%
1 86
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
44.4%
. 129
33.3%
1 86
22.2%

Use_of_vasoactives
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
124 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 124
96.1%
1.0 5
 
3.9%

Length

2023-12-14T11:18:04.167926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.215107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 124
96.1%
1.0 5
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 253
65.4%
. 129
33.3%
1 5
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253
98.1%
1 5
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253
65.4%
. 129
33.3%
1 5
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253
65.4%
. 129
33.3%
1 5
 
1.3%

Ventil_Mecan
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
122 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 122
94.6%
1.0 7
 
5.4%

Length

2023-12-14T11:18:04.266069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.313206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 122
94.6%
1.0 7
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 251
64.9%
. 129
33.3%
1 7
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251
97.3%
1 7
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251
64.9%
. 129
33.3%
1 7
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251
64.9%
. 129
33.3%
1 7
 
1.8%

Number_of_Organ_Failures
Categorical

IMBALANCE 

Distinct5
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
118 
1.0
 
6
2.0
 
3
5.0
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.6%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 118
91.5%
1.0 6
 
4.7%
2.0 3
 
2.3%
5.0 1
 
0.8%
3.0 1
 
0.8%

Length

2023-12-14T11:18:04.364000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.415587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 118
91.5%
1.0 6
 
4.7%
2.0 3
 
2.3%
5.0 1
 
0.8%
3.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 247
63.8%
. 129
33.3%
1 6
 
1.6%
2 3
 
0.8%
5 1
 
0.3%
3 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 247
95.7%
1 6
 
2.3%
2 3
 
1.2%
5 1
 
0.4%
3 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 247
63.8%
. 129
33.3%
1 6
 
1.6%
2 3
 
0.8%
5 1
 
0.3%
3 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 247
63.8%
. 129
33.3%
1 6
 
1.6%
2 3
 
0.8%
5 1
 
0.3%
3 1
 
0.3%

ACLF_Grade
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
121 
2.0
 
3
3.0
 
3
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 121
93.8%
2.0 3
 
2.3%
3.0 3
 
2.3%
1.0 2
 
1.6%

Length

2023-12-14T11:18:04.473134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.522497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 121
93.8%
2.0 3
 
2.3%
3.0 3
 
2.3%
1.0 2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 250
64.6%
. 129
33.3%
2 3
 
0.8%
3 3
 
0.8%
1 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 250
96.9%
2 3
 
1.2%
3 3
 
1.2%
1 2
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 250
64.6%
. 129
33.3%
2 3
 
0.8%
3 3
 
0.8%
1 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 250
64.6%
. 129
33.3%
2 3
 
0.8%
3 3
 
0.8%
1 2
 
0.5%

Ascita_post_op
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
106 
1.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 106
82.2%
1.0 23
 
17.8%

Length

2023-12-14T11:18:04.577069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.624314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 106
82.2%
1.0 23
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 235
60.7%
. 129
33.3%
1 23
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 235
91.1%
1 23
 
8.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 235
60.7%
. 129
33.3%
1 23
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235
60.7%
. 129
33.3%
1 23
 
5.9%

HRS_post_op
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
125 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 125
96.9%
1.0 4
 
3.1%

Length

2023-12-14T11:18:04.676576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.723670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 125
96.9%
1.0 4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 254
65.6%
. 129
33.3%
1 4
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 254
98.4%
1 4
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 254
65.6%
. 129
33.3%
1 4
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254
65.6%
. 129
33.3%
1 4
 
1.0%

disf_renala_nonHRS_post_op
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
115 
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 115
89.1%
1.0 14
 
10.9%

Length

2023-12-14T11:18:04.774212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.821789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 115
89.1%
1.0 14
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244
94.6%
1 14
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244
63.0%
. 129
33.3%
1 14
 
3.6%

HE_post_op
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
126 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 126
97.7%
1.0 3
 
2.3%

Length

2023-12-14T11:18:04.873979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:04.920836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 126
97.7%
1.0 3
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 255
65.9%
. 129
33.3%
1 3
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255
98.8%
1 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255
65.9%
. 129
33.3%
1 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255
65.9%
. 129
33.3%
1 3
 
0.8%

JaundiceBTgt3
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
124 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 124
96.1%
1.0 5
 
3.9%

Length

2023-12-14T11:18:04.972884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:05.020488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 124
96.1%
1.0 5
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 253
65.4%
. 129
33.3%
1 5
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253
98.1%
1 5
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253
65.4%
. 129
33.3%
1 5
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253
65.4%
. 129
33.3%
1 5
 
1.3%

HDS_postop
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
127 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 127
98.4%
1.0 2
 
1.6%

Length

2023-12-14T11:18:05.070414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:05.119282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 127
98.4%
1.0 2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 256
66.1%
. 129
33.3%
1 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 256
99.2%
1 2
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 256
66.1%
. 129
33.3%
1 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 256
66.1%
. 129
33.3%
1 2
 
0.5%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
108 
1.0
21 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 108
83.7%
1.0 21
 
16.3%

Length

2023-12-14T11:18:05.169650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:05.218324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 108
83.7%
1.0 21
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 237
61.2%
. 129
33.3%
1 21
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 237
91.9%
1 21
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 237
61.2%
. 129
33.3%
1 21
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 237
61.2%
. 129
33.3%
1 21
 
5.4%

Post_op_decomp
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
113 
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 113
87.6%
1.0 16
 
12.4%

Length

2023-12-14T11:18:05.269289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:05.317594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 113
87.6%
1.0 16
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 242
62.5%
. 129
33.3%
1 16
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 242
93.8%
1 16
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 242
62.5%
. 129
33.3%
1 16
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 242
62.5%
. 129
33.3%
1 16
 
4.1%

Recidive
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
88 
1.0
41 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 88
68.2%
1.0 41
31.8%

Length

2023-12-14T11:18:05.368172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:05.416610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88
68.2%
1.0 41
31.8%

Most occurring characters

ValueCountFrequency (%)
0 217
56.1%
. 129
33.3%
1 41
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217
84.1%
1 41
 
15.9%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 217
56.1%
. 129
33.3%
1 41
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 217
56.1%
. 129
33.3%
1 41
 
10.6%

Hospitalisation_days
Real number (ℝ)

Distinct27
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.449612
Minimum2
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:05.467381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median9
Q312
95-th percentile25.2
Maximum86
Range84
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.7250974
Coefficient of variation (CV)0.84938224
Kurtosis30.321972
Mean11.449612
Median Absolute Deviation (MAD)2
Skewness4.7834576
Sum1477
Variance94.577519
MonotonicityNot monotonic
2023-12-14T11:18:05.526182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7 22
17.1%
9 15
11.6%
12 13
10.1%
8 12
9.3%
11 10
7.8%
6 9
 
7.0%
10 8
 
6.2%
5 7
 
5.4%
4 5
 
3.9%
14 4
 
3.1%
Other values (17) 24
18.6%
ValueCountFrequency (%)
2 1
 
0.8%
4 5
 
3.9%
5 7
 
5.4%
6 9
7.0%
7 22
17.1%
8 12
9.3%
9 15
11.6%
10 8
 
6.2%
11 10
7.8%
12 13
10.1%
ValueCountFrequency (%)
86 1
0.8%
57 1
0.8%
41 1
0.8%
32 1
0.8%
30 1
0.8%
28 1
0.8%
26 1
0.8%
24 1
0.8%
21 1
0.8%
20 2
1.6%

APRI
Real number (ℝ)

Distinct126
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2539435
Minimum0.096474954
Maximum7.452381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:05.588507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.096474954
5-th percentile0.26135304
Q10.46153846
median0.88275862
Q31.4693878
95-th percentile3.8351993
Maximum7.452381
Range7.355906
Interquartile range (IQR)1.0078493

Descriptive statistics

Standard deviation1.2951999
Coefficient of variation (CV)1.0329013
Kurtosis8.5681329
Mean1.2539435
Median Absolute Deviation (MAD)0.45522995
Skewness2.704376
Sum161.75871
Variance1.6775428
MonotonicityNot monotonic
2023-12-14T11:18:05.660500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.571428571 2
 
1.6%
1.011904762 2
 
1.6%
1.469387755 2
 
1.6%
1.761546724 1
 
0.8%
1.721170396 1
 
0.8%
0.424710425 1
 
0.8%
2.704761905 1
 
0.8%
0.779220779 1
 
0.8%
0.38961039 1
 
0.8%
0.65681445 1
 
0.8%
Other values (116) 116
89.9%
ValueCountFrequency (%)
0.096474954 1
0.8%
0.167519777 1
0.8%
0.183486239 1
0.8%
0.184331797 1
0.8%
0.238095238 1
0.8%
0.242488139 1
0.8%
0.25871766 1
0.8%
0.265306122 1
0.8%
0.270078181 1
0.8%
0.272727273 1
0.8%
ValueCountFrequency (%)
7.452380952 1
0.8%
6.803699897 1
0.8%
6.763848397 1
0.8%
5.518925519 1
0.8%
4.144869215 1
0.8%
4.076655052 1
0.8%
3.896103896 1
0.8%
3.743842365 1
0.8%
3.568010936 1
0.8%
3.387755102 1
0.8%

APRI_0.74
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
75 
0.0
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 75
58.1%
0.0 54
41.9%

Length

2023-12-14T11:18:05.725114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:05.774067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 75
58.1%
0.0 54
41.9%

Most occurring characters

ValueCountFrequency (%)
0 183
47.3%
. 129
33.3%
1 75
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 183
70.9%
1 75
29.1%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 183
47.3%
. 129
33.3%
1 75
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 183
47.3%
. 129
33.3%
1 75
19.4%

FIB4
Real number (ℝ)

UNIQUE 

Distinct129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6584581
Minimum0.15400948
Maximum11.640496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:05.831652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.15400948
5-th percentile0.38398601
Q10.72049689
median1.1561562
Q32.0329588
95-th percentile4.3061329
Maximum11.640496
Range11.486486
Interquartile range (IQR)1.3124619

Descriptive statistics

Standard deviation1.6794548
Coefficient of variation (CV)1.0126604
Kurtosis15.519283
Mean1.6584581
Median Absolute Deviation (MAD)0.55870842
Skewness3.4620218
Sum213.9411
Variance2.8205686
MonotonicityNot monotonic
2023-12-14T11:18:05.903379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.382445141 1
 
0.8%
2.714285714 1
 
0.8%
0.866702071 1
 
0.8%
0.941176471 1
 
0.8%
1.000100216 1
 
0.8%
1.119736842 1
 
0.8%
1.890575585 1
 
0.8%
1.446500868 1
 
0.8%
0.75 1
 
0.8%
0.292561983 1
 
0.8%
Other values (119) 119
92.2%
ValueCountFrequency (%)
0.154009475 1
0.8%
0.249382202 1
0.8%
0.277238551 1
0.8%
0.292561983 1
0.8%
0.30097166 1
0.8%
0.318287576 1
0.8%
0.378059072 1
0.8%
0.392876417 1
0.8%
0.397959184 1
0.8%
0.424354244 1
0.8%
ValueCountFrequency (%)
11.64049587 1
0.8%
10.4358209 1
0.8%
7.573333333 1
0.8%
6.097674419 1
0.8%
5.684323167 1
0.8%
4.8 1
0.8%
4.542510121 1
0.8%
3.951566952 1
0.8%
3.75323913 1
0.8%
3.737373737 1
0.8%

FIB4_3.74
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
120 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 120
93.0%
1.0 9
 
7.0%

Length

2023-12-14T11:18:05.966862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:06.014381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 120
93.0%
1.0 9
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
96.5%
1 9
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

NLR
Real number (ℝ)

Distinct128
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1364496
Minimum0.45797923
Maximum54.695652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:06.071145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.45797923
5-th percentile1.1210763
Q11.8597561
median2.484252
Q34
95-th percentile10.164248
Maximum54.695652
Range54.237673
Interquartile range (IQR)2.1402439

Descriptive statistics

Standard deviation6.1536057
Coefficient of variation (CV)1.487654
Kurtosis41.09468
Mean4.1364496
Median Absolute Deviation (MAD)0.92419874
Skewness5.84002
Sum533.602
Variance37.866864
MonotonicityNot monotonic
2023-12-14T11:18:06.141918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2
 
1.6%
1.015384615 1
 
0.8%
2.894736842 1
 
0.8%
2.224358974 1
 
0.8%
1.739130435 1
 
0.8%
2.891666667 1
 
0.8%
2.375 1
 
0.8%
19.48529412 1
 
0.8%
1.37164751 1
 
0.8%
1.928571429 1
 
0.8%
Other values (118) 118
91.5%
ValueCountFrequency (%)
0.457979226 1
0.8%
0.700704225 1
0.8%
0.711538462 1
0.8%
0.872817955 1
0.8%
1.015384615 1
0.8%
1.033492823 1
0.8%
1.101522843 1
0.8%
1.150406504 1
0.8%
1.153409091 1
0.8%
1.221428571 1
0.8%
ValueCountFrequency (%)
54.69565217 1
0.8%
36.22047244 1
0.8%
19.67741935 1
0.8%
19.48529412 1
0.8%
14.325 1
0.8%
13.953125 1
0.8%
10.49019608 1
0.8%
9.675324675 1
0.8%
9.578947368 1
0.8%
9.205479452 1
0.8%

NLR_2.26
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
76 
0.0
53 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 76
58.9%
0.0 53
41.1%

Length

2023-12-14T11:18:06.277593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:06.326197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 76
58.9%
0.0 53
41.1%

Most occurring characters

ValueCountFrequency (%)
0 182
47.0%
. 129
33.3%
1 76
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 182
70.5%
1 76
29.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 182
47.0%
. 129
33.3%
1 76
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 182
47.0%
. 129
33.3%
1 76
19.6%

ALBI
Real number (ℝ)

Distinct118
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.6526647
Minimum-4.0213832
Maximum-1.2612225
Zeros0
Zeros (%)0.0%
Negative129
Negative (%)100.0%
Memory size1.1 KiB
2023-12-14T11:18:06.381866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-4.0213832
5-th percentile-3.595572
Q1-2.9633024
median-2.686463
Q3-2.3460236
95-th percentile-1.7034069
Maximum-1.2612225
Range2.7601607
Interquartile range (IQR)0.61727878

Descriptive statistics

Standard deviation0.55708667
Coefficient of variation (CV)-0.21001021
Kurtosis0.10864819
Mean-2.6526647
Median Absolute Deviation (MAD)0.34043939
Skewness0.14041998
Sum-342.19375
Variance0.31034555
MonotonicityNot monotonic
2023-12-14T11:18:06.450261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.952057861 2
 
1.6%
-3.218902364 2
 
1.6%
-3.037257861 2
 
1.6%
-3.081442742 2
 
1.6%
-2.689419955 2
 
1.6%
-2.679422567 2
 
1.6%
-2.792902364 2
 
1.6%
-2.485042742 2
 
1.6%
-2.794822511 2
 
1.6%
-3.048502364 2
 
1.6%
Other values (108) 109
84.5%
ValueCountFrequency (%)
-4.021383176 1
0.8%
-3.936183176 1
0.8%
-3.815302364 1
0.8%
-3.721582364 1
0.8%
-3.718857861 1
0.8%
-3.633657861 1
0.8%
-3.595697805 1
0.8%
-3.595383176 1
0.8%
-3.561298469 1
0.8%
-3.463257861 1
0.8%
ValueCountFrequency (%)
-1.261222511 1
0.8%
-1.345823598 1
0.8%
-1.372322483 1
0.8%
-1.382926482 1
0.8%
-1.573742714 1
0.8%
-1.588323849 1
0.8%
-1.695742511 1
0.8%
-1.714903395 1
0.8%
-1.732750794 1
0.8%
-1.770502364 1
0.8%

ALBI__2.71
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
82 
0.0
47 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 82
63.6%
0.0 47
36.4%

Length

2023-12-14T11:18:06.512457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:06.560643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 82
63.6%
0.0 47
36.4%

Most occurring characters

ValueCountFrequency (%)
0 176
45.5%
. 129
33.3%
1 82
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 176
68.2%
1 82
31.8%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 176
45.5%
. 129
33.3%
1 82
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 176
45.5%
. 129
33.3%
1 82
21.2%

eLIFT
Real number (ℝ)

Distinct16
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.139535
Minimum3
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:06.604950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median13
Q315
95-th percentile16
Maximum18
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.234927
Coefficient of variation (CV)0.26647866
Kurtosis-0.091421545
Mean12.139535
Median Absolute Deviation (MAD)2
Skewness-0.63504167
Sum1566
Variance10.464753
MonotonicityNot monotonic
2023-12-14T11:18:06.660813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 23
17.8%
11 15
11.6%
15 15
11.6%
16 15
11.6%
14 13
10.1%
12 11
8.5%
9 9
 
7.0%
7 6
 
4.7%
10 5
 
3.9%
8 5
 
3.9%
Other values (6) 12
9.3%
ValueCountFrequency (%)
3 1
 
0.8%
4 1
 
0.8%
5 4
 
3.1%
6 2
 
1.6%
7 6
 
4.7%
8 5
 
3.9%
9 9
7.0%
10 5
 
3.9%
11 15
11.6%
12 11
8.5%
ValueCountFrequency (%)
18 3
 
2.3%
17 1
 
0.8%
16 15
11.6%
15 15
11.6%
14 13
10.1%
13 23
17.8%
12 11
8.5%
11 15
11.6%
10 5
 
3.9%
9 9
 
7.0%

eLIFT_12.5
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
80 
0.0
49 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 80
62.0%
0.0 49
38.0%

Length

2023-12-14T11:18:06.721600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:06.769570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 80
62.0%
0.0 49
38.0%

Most occurring characters

ValueCountFrequency (%)
0 178
46.0%
. 129
33.3%
1 80
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178
69.0%
1 80
31.0%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178
46.0%
. 129
33.3%
1 80
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178
46.0%
. 129
33.3%
1 80
20.7%

CRP_Albumine_ratio
Real number (ℝ)

INFINITE 

Distinct111
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite2
Infinite (%)1.6%
Meaninf
Minimum0.0023980815
Maximuminf
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:06.827244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0023980815
5-th percentile0.04544486
Q10.1
median0.28378378
Q30.49239268
95-th percentile1.8864007
Maximuminf
Rangeinf
Interquartile range (IQR)0.39239268

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.1880391
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2023-12-14T11:18:06.899750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2837837838 5
 
3.9%
0.4122357294 3
 
2.3%
0.4220508658 3
 
2.3%
0.4431534091 3
 
2.3%
0.9026315789 2
 
1.6%
0.180952381 2
 
1.6%
inf 2
 
1.6%
0.6330762987 2
 
1.6%
0.4261090472 2
 
1.6%
0.454516317 2
 
1.6%
Other values (101) 103
79.8%
ValueCountFrequency (%)
0.002398081535 1
0.8%
0.004524886878 1
0.8%
0.01178781925 1
0.8%
0.01555555556 1
0.8%
0.01670644391 1
0.8%
0.03036437247 1
0.8%
0.04366812227 1
0.8%
0.04810996564 1
0.8%
0.05263157895 1
0.8%
0.06688963211 1
0.8%
ValueCountFrequency (%)
inf 2
1.6%
5.317919075 1
0.8%
2.894736842 1
0.8%
2.872807018 1
0.8%
1.963636364 1
0.8%
1.955882353 1
0.8%
1.782178218 1
0.8%
1.707317073 1
0.8%
1.565116279 1
0.8%
1.463414634 1
0.8%

FIB_3.74
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
120 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 120
93.0%
1.0 9
 
7.0%

Length

2023-12-14T11:18:06.961848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.009607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 120
93.0%
1.0 9
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
96.5%
1 9
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
64.3%
. 129
33.3%
1 9
 
2.3%

VARSTA_ELIFT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3.0
129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 129
100.0%

Length

2023-12-14T11:18:07.059827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.106134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 129
100.0%

Most occurring characters

ValueCountFrequency (%)
3 129
33.3%
. 129
33.3%
0 129
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 129
50.0%
0 129
50.0%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 129
33.3%
. 129
33.3%
0 129
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 129
33.3%
. 129
33.3%
0 129
33.3%

SEX_ELIFT
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
101 
0.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 101
78.3%
0.0 28
 
21.7%

Length

2023-12-14T11:18:07.155206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.201857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 101
78.3%
0.0 28
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 157
40.6%
. 129
33.3%
1 101
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
60.9%
1 101
39.1%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
40.6%
. 129
33.3%
1 101
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
40.6%
. 129
33.3%
1 101
26.1%

AST_ELIFT
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0.0
59 
2.0
50 
4.0
20 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row4.0
3rd row0.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 59
45.7%
2.0 50
38.8%
4.0 20
 
15.5%

Length

2023-12-14T11:18:07.253755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.303102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59
45.7%
2.0 50
38.8%
4.0 20
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 188
48.6%
. 129
33.3%
2 50
 
12.9%
4 20
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 188
72.9%
2 50
 
19.4%
4 20
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 188
48.6%
. 129
33.3%
2 50
 
12.9%
4 20
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 188
48.6%
. 129
33.3%
2 50
 
12.9%
4 20
 
5.2%

GGT_ELIFT
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
64 
0.0
33 
2.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 64
49.6%
0.0 33
25.6%
2.0 32
24.8%

Length

2023-12-14T11:18:07.358061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.406806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 64
49.6%
0.0 33
25.6%
2.0 32
24.8%

Most occurring characters

ValueCountFrequency (%)
0 162
41.9%
. 129
33.3%
1 64
 
16.5%
2 32
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
62.8%
1 64
 
24.8%
2 32
 
12.4%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
41.9%
. 129
33.3%
1 64
 
16.5%
2 32
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
41.9%
. 129
33.3%
1 64
 
16.5%
2 32
 
8.3%

IP_ELIFT
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
4.0
79 
2.0
37 
0.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 79
61.2%
2.0 37
28.7%
0.0 13
 
10.1%

Length

2023-12-14T11:18:07.461674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.510731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 79
61.2%
2.0 37
28.7%
0.0 13
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 142
36.7%
. 129
33.3%
4 79
20.4%
2 37
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142
55.0%
4 79
30.6%
2 37
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142
36.7%
. 129
33.3%
4 79
20.4%
2 37
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142
36.7%
. 129
33.3%
4 79
20.4%
2 37
 
9.6%

PLT_ELIFT
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
4.0
92 
1.0
24 
0.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters387
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
4.0 92
71.3%
1.0 24
 
18.6%
0.0 13
 
10.1%

Length

2023-12-14T11:18:07.565425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T11:18:07.614471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 92
71.3%
1.0 24
 
18.6%
0.0 13
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 142
36.7%
. 129
33.3%
4 92
23.8%
1 24
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
66.7%
Other Punctuation 129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142
55.0%
4 92
35.7%
1 24
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142
36.7%
. 129
33.3%
4 92
23.8%
1 24
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142
36.7%
. 129
33.3%
4 92
23.8%
1 24
 
6.2%

df_index
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65
Minimum1
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-14T11:18:07.675546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.4
Q133
median65
Q397
95-th percentile122.6
Maximum129
Range128
Interquartile range (IQR)64

Descriptive statistics

Standard deviation37.383151
Coefficient of variation (CV)0.5751254
Kurtosis-1.2
Mean65
Median Absolute Deviation (MAD)32
Skewness0
Sum8385
Variance1397.5
MonotonicityStrictly increasing
2023-12-14T11:18:07.750342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
66 1
 
0.8%
96 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
Other values (119) 119
92.2%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
129 1
0.8%
128 1
0.8%
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%

Interactions

2023-12-14T11:17:54.375852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:16:58.669448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.279168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.886015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.515981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.150484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.835996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.532225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:10.093346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.735993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.386762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:15.019786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.624318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.233830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.910434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.497035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:23.044771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.577390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:26.076094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.582027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.210274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.776018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.264806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.904757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.463146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:37.070347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.722679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.212474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.896161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.459429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:45.007734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.546803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:48.121412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.670068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.240915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.859233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.417032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:16:58.711821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.321457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.928722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.559881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.195545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.880579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.572366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:10.136571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.780789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.430027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:15.062740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.667340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.277403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.953531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.537859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:23.084379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.618564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:26.116484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.623195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.250303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.816364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.308096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.945222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.504597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:37.113637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.763668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.256491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.936898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.498897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:45.048848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.588045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:48.162684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.711377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.282534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.899487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.457732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:16:58.755153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.362841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.969774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.604089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.239139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.924304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.612035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:10.178114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.824615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.473992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:15.104142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.707683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.321472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.994920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.578708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:23.123631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.659940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:26.221961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.664942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.290467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.856126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.349517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.985470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.546356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:37.155539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.803146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.299494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.977448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.540768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:45.091082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.627456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:48.201557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.750763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.325111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.939883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.501528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:16:58.797773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.406155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:02.012860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.650324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.357675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.969897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.653399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:10.221972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.871632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.518195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:15.146641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.752112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.365434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:20.039162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.619165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:23.163994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.701660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-12-14T11:17:09.885915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.445890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.157850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:14.803610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.409407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.020320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.615948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.278117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:22.835221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.377413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:25.868550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.380436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:28.992903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.569850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.058064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.687150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.257008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:36.858564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.504936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.005910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.672149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.245181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:44.803809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.334594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:47.913789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.466045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.032213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.643382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.168338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:55.805849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.104960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.713201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.268822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:04.967508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.654690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.350970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:09.925838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.488285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.201660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:14.845859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.451532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.061610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.657387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.321231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:22.875728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.415460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:25.907651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.418820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.034415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.608989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.097782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.728990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.296637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:36.899432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.547173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.045285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.717294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.287937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:44.843882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.375477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:47.954297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.504713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.073507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.685293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.208618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:55.847706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.146735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.756902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.311329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.013027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.699957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.396241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:09.967235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.604031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.246297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:14.890464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.494819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.102974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.701429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.363415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:22.918875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.455226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:25.948685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.458010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.077838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.649081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.136973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.772429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.336722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:36.941945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.590755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.086408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.761316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.330327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:44.883507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.417511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:47.996047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.546433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.113342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.727536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.249873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:55.894186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.193074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.801388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.429057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.060174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.746972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.443570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:10.011878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.649710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.295571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:14.934905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.539187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.148525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.747993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.410386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:22.963456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.497758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:25.993716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.500215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.123512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.693620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.180364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.817585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.380021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:36.987048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.636112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.130057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.808536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.375535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:44.926896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.461708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:48.039145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.589702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.156439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.773168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.293869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:55.934845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:00.234120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:01.842873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:03.472087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:05.104225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:06.791259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:08.487529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:10.052071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:11.693142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:13.340829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:14.977134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:16.581111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:18.190275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:19.865089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:21.452307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:23.002771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:24.536797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:26.034820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:27.542573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:29.165943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:30.734079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:32.223828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:33.860556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:35.420542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:37.028864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:38.678696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:40.170769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:41.851193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:43.415934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:44.966546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:46.503953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:48.080340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:49.630033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:51.196665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:52.815366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-14T11:17:54.333739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2023-12-14T11:17:56.098819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-14T11:17:56.408303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SurvivalCHILD_scoreCHILD_clasifNr_nodulesBCLC_classAgeSexEtiologyDAA_before_dgDAA_after_dgEVGradegastricVHTP_gastropathyprimary_prophylaxis_0nu_1dasecondary_prophylaxyPVT_ThrombosisSpleenSplenomegaly_0noPlateletsPlatelets_less_100000splenomegaly_Tr_sub_10HEAscitesLiverStiffnesFS_19kPaFS_13.6CSPHHVPGMELD_8CSPH_MELD_10MELD_more_than_10MELDMELDNaASTALTFAGGTBilirubineCreatinineNeutrophilsLymphocytesNatriumIPINRAlbumineCRPAFPPrevious_decompSegmentMajor_surgerySize_each_noduleSize_totaltu_more_than_3LaparoscopyRFA_IOPAnesthesia_Duration_minBlood_LossSurgery_Duration_minTransfusion_IOPIOP_ComplicationsResection_MarginMicrovascular_InvasionMIlan_probabilityUse_of_vasoactivesVentil_MecanNumber_of_Organ_FailuresACLF_GradeAscita_post_opHRS_post_opdisf_renala_nonHRS_post_opHE_post_opJaundiceBTgt3HDS_postopInfection_post_opPost_op_decompRecidiveHospitalisation_daysAPRIAPRI_0.74FIB4FIB4_3.74NLRNLR_2.26ALBIALBI__2.71eLIFTeLIFT_12.5CRP_Albumine_ratioFIB_3.74VARSTA_ELIFTSEX_ELIFTAST_ELIFTGGT_ELIFTIP_ELIFTPLT_ELIFTdf_index
00.05.00.01.02.057.01.02.00.00.00.00.00.00.00.00.00.0150.000000187.01.01.00.00.010.10.00.00.08.01.00.00.07.08.020.011.0266.038.00.70.000.660.65138.087.01.104.31.77261433.200.02.00.03.55.51.00.00.0130.0200.0120.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.09.00.6568140.02.3824450.01.0153850.0-2.9520581.011.00.00.4122360.03.01.00.01.02.04.01.0
10.06.00.01.02.077.02.02.00.00.00.00.00.00.00.00.00.0129.4712641179.00.00.00.00.013.90.00.01.012.00.00.00.07.07.073.047.0154.072.00.50.664.122.05142.095.01.032.91.7726148.750.02.00.03.55.31.00.00.0150.00.0125.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.01.01.00.057.01.1652041.01.3362650.02.0097560.0-1.8557020.011.00.00.6112460.03.00.04.01.02.01.02.0
20.06.00.03.03.069.01.03.00.00.00.00.00.00.00.00.00.0122.000000196.01.00.00.00.020.41.01.01.010.01.01.01.010.010.034.018.0363.068.00.80.732.891.88141.065.01.373.36.48000045.000.02.00.03.50.40.00.00.0120.050.0120.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.01.07.01.0119051.02.7152780.01.5372340.0-2.0617830.013.01.01.9636360.03.01.00.01.04.04.03.0
30.07.01.03.02.062.01.03.00.00.00.00.00.00.00.00.01.0129.4712641172.00.00.00.00.038.01.01.01.014.01.00.00.09.09.054.054.0544.0114.00.91.1346.001.27144.083.01.152.41.7726148.000.02.00.03.57.01.00.00.0140.0300.0120.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.012.00.8970101.00.7209300.036.2204721.0-1.2612230.013.01.00.7385890.03.01.02.02.04.01.04.0
41.05.00.01.02.072.02.02.00.00.00.00.00.00.00.00.00.0110.0000000274.00.00.00.00.021.11.01.00.09.00.00.00.07.09.041.033.0153.0114.00.80.005.602.50141.087.01.104.21.77261410.000.01.00.03.56.01.00.00.0130.0400.0120.01.01.01.01.00.00.00.00.00.01.00.00.00.00.00.00.01.00.019.00.4275290.00.6529530.02.2400000.0-2.8285831.09.00.00.4220510.03.00.02.02.02.00.05.0
50.07.01.01.02.068.02.01.00.00.01.01.00.01.01.00.00.0160.000000170.01.01.00.00.011.40.00.01.015.01.01.01.012.012.036.051.0527.026.01.30.563.201.04142.056.01.530.01.7726142.800.01.00.03.53.00.00.00.0100.0300.090.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.08.01.4693881.01.3714290.03.0769231.0-2.6042200.013.01.0inf0.03.00.02.00.04.04.06.0
60.05.00.01.02.061.01.02.00.01.00.00.00.00.00.00.00.0160.0000001150.00.00.00.00.013.00.00.00.05.01.00.01.019.019.039.054.0203.062.00.33.132.832.46140.073.01.183.71.4500004.200.02.00.03.56.31.00.00.0220.0700.0200.01.01.00.00.00.00.00.01.01.00.00.01.00.00.00.01.00.00.016.00.7428571.00.5874070.01.1504070.0-2.6837230.015.01.00.3918920.03.01.02.01.04.04.07.0
70.06.00.04.03.057.01.01.00.00.00.00.00.00.00.00.00.077.0000000279.00.00.00.00.06.00.00.00.05.00.00.00.06.06.018.09.0275.065.00.50.913.504.01141.0103.00.982.81.7726148.750.04.01.03.55.71.00.01.0130.0300.0120.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.07.00.1843320.00.8172040.00.8728180.0-1.7705020.05.00.00.6330760.03.01.00.01.00.00.08.0
80.08.01.01.02.068.02.02.00.00.00.00.00.00.00.00.00.0160.000000166.01.01.00.01.022.81.01.01.018.01.01.01.013.013.057.044.0434.022.02.20.442.261.12141.054.01.412.81.7726148.751.01.00.03.56.51.00.00.0130.0350.0120.01.01.00.01.00.00.00.00.00.01.00.00.00.01.00.01.01.00.020.02.4675321.02.6694210.02.0178570.0-1.3458240.013.01.00.6330760.03.00.02.00.04.04.09.0
90.09.01.01.02.062.01.03.00.00.00.00.00.00.00.00.00.0125.0000001115.00.00.00.00.055.11.01.01.014.01.01.01.016.026.063.040.0346.026.03.20.995.923.44104.054.81.520.01.772614957.000.02.00.03.53.00.00.01.0210.01000.0210.01.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.012.01.5652171.01.6982610.01.7209300.0-2.3460240.014.01.0inf0.03.01.02.00.04.04.010.0
SurvivalCHILD_scoreCHILD_clasifNr_nodulesBCLC_classAgeSexEtiologyDAA_before_dgDAA_after_dgEVGradegastricVHTP_gastropathyprimary_prophylaxis_0nu_1dasecondary_prophylaxyPVT_ThrombosisSpleenSplenomegaly_0noPlateletsPlatelets_less_100000splenomegaly_Tr_sub_10HEAscitesLiverStiffnesFS_19kPaFS_13.6CSPHHVPGMELD_8CSPH_MELD_10MELD_more_than_10MELDMELDNaASTALTFAGGTBilirubineCreatinineNeutrophilsLymphocytesNatriumIPINRAlbumineCRPAFPPrevious_decompSegmentMajor_surgerySize_each_noduleSize_totaltu_more_than_3LaparoscopyRFA_IOPAnesthesia_Duration_minBlood_LossSurgery_Duration_minTransfusion_IOPIOP_ComplicationsResection_MarginMicrovascular_InvasionMIlan_probabilityUse_of_vasoactivesVentil_MecanNumber_of_Organ_FailuresACLF_GradeAscita_post_opHRS_post_opdisf_renala_nonHRS_post_opHE_post_opJaundiceBTgt3HDS_postopInfection_post_opPost_op_decompRecidiveHospitalisation_daysAPRIAPRI_0.74FIB4FIB4_3.74NLRNLR_2.26ALBIALBI__2.71eLIFTeLIFT_12.5CRP_Albumine_ratioFIB_3.74VARSTA_ELIFTSEX_ELIFTAST_ELIFTGGT_ELIFTIP_ELIFTPLT_ELIFTdf_index
1191.05.00.02.03.069.02.01.01.00.00.00.00.00.00.00.00.0119.0000000139.00.00.00.00.00.00.00.00.04.00.00.00.07.08.0331.0265.048.017.01.5000.6312.580.23136.859.01.444.013.3800006.110.04.00.05.06.00.01.01.0120.0400.0120.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.014.06.8037001.01.2400710.054.6956521.0-2.4865221.015.01.00.8428930.03.00.04.00.04.04.0120.0
1201.05.00.01.02.063.01.03.00.00.00.00.00.00.00.00.00.0119.000000052.01.00.00.00.00.00.00.00.08.01.00.00.08.08.035.233.088.773.60.5601.034.571.32139.889.01.084.940.1500008.750.02.00.06.06.00.00.01.0175.02000.0175.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.015.01.9340661.02.5846150.03.4621211.0-3.5612981.013.01.00.0303640.03.01.02.01.02.04.0121.0
1211.010.03.01.03.054.02.02.00.00.01.01.00.00.00.00.00.0140.000000124.01.01.00.01.013.90.00.00.04.01.01.01.014.019.062.624.2103.023.03.4000.831.992.84138.040.02.002.990.2000005.460.01.00.03.03.00.01.01.0135.0200.0135.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.016.07.4523811.011.6404961.00.7007040.0-1.3829261.013.01.00.0668901.03.00.02.00.04.04.0122.0
1221.05.00.01.02.066.01.02.00.00.00.00.00.00.00.00.00.0129.4712641142.00.00.00.00.015.70.01.01.013.00.00.00.06.06.030.017.061.358.00.5180.825.671.07140.092.01.004.310.3100008.750.01.00.07.07.00.00.01.0100.0700.0100.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.05.00.6036220.01.6404310.05.2990651.0-3.0468851.011.01.00.0719260.03.01.00.01.02.04.0123.0
1231.05.00.01.02.066.01.02.00.00.00.00.00.00.00.00.00.0167.0000001117.00.00.00.01.013.90.00.01.015.01.00.00.09.09.022.019.092.071.50.7900.713.830.50137.079.01.203.851.7726148.750.02.00.06.06.00.01.01.080.0150.080.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.014.00.5372410.01.3063430.07.6600001.0-2.5339891.013.01.00.4604190.03.01.00.01.04.04.0124.0
1241.05.00.01.01.070.01.03.00.00.00.00.00.00.00.00.00.0119.0000000145.00.00.00.00.013.90.00.00.09.01.01.01.011.014.029.027.084.072.02.0600.912.621.45138.056.01.504.161.7726148.750.01.00.04.04.00.01.01.0150.0150.0150.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.010.00.5714290.01.0370370.01.8068971.0-2.5233901.013.01.00.4261090.03.01.00.01.04.04.0125.0
1250.05.00.01.01.057.01.01.00.00.00.00.00.00.00.00.00.0119.000000082.01.00.00.00.031.21.01.00.09.01.00.00.08.010.0117.0139.0103.0291.01.3600.661.951.47139.877.01.204.341.7726148.750.02.00.05.05.00.00.01.0270.0200.0270.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.030.04.0766551.01.1702050.01.3265311.0-2.7957671.018.01.00.4084360.03.01.04.02.04.04.0126.0
1261.06.00.01.01.067.02.02.00.00.00.00.00.00.00.00.00.0129.4712641164.00.00.00.00.013.90.00.01.013.00.00.00.07.08.026.2139.098.079.01.2300.627.801.95142.996.01.033.231.7726148.750.01.00.02.32.30.01.01.060.0100.060.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.011.00.4564460.00.1540090.04.0000001.0-1.8788451.010.01.00.5487970.03.00.00.01.02.04.0127.0
1271.05.00.01.01.063.01.02.00.00.00.00.00.00.00.00.00.0129.4712641117.00.00.00.00.013.90.00.01.012.01.01.00.010.010.055.059.045.438.80.8800.653.350.70138.065.01.323.143.2300008.750.02.00.03.03.00.00.01.0150.0300.0150.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.07.01.3431011.01.0039110.04.7857141.0-1.8981441.015.01.01.0286620.03.01.02.01.04.04.0128.0
1281.05.00.01.01.071.01.01.00.00.00.00.00.00.00.00.00.0119.0000000147.00.00.00.00.013.90.00.00.04.01.00.00.09.09.025.398.057.042.00.7500.676.312.54140.072.01.264.106.0000002.000.01.00.02.52.50.00.01.0130.0100.0130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.05.00.4917400.00.2493820.02.4842521.0-2.7618821.013.01.01.4634150.03.01.00.01.04.04.0129.0